CAV-1, StatSa Signaling, and Estrogen-Dependent Breast Cancer The human Caveolin-1 (Cav-1) gene acts as a mammary gland tumor suppressor. We have previously identified Cav-1 inactivating (dominant-negative (DN)) mutations in up to 35 % of estrogen receptor (ER) positive breast cancer patients. Our hypothesis is that up-regulation of ER levels and activity are caused by Cav-1 inactivating mutations. As Cav-1 functions as an inhibitor of the Jak-2 kinase, we propose that StatSa activation is the mechanism by which loss of Cav-1 function results in increased ER-alpha levels. In support of this hypothesis, we present novel evidence that StatSa activation is sufficient to upregulate ER-alpha levels in ER-negative human breast cancer cells. As such, our preliminary studies have now defined a novel signaling pathway leading to breast cancer: Cav-1 gene inactivation (DN-mutations) --> StatSa activation --> ER-alpha upreoulation -> Cvclin D1 over-expression. The three Specific Aims of the project are: 1) Determine the role of StatSa activation and ER-alpha in Cav-1-related mammary hyperplasia. proliferation, and 3D lumen formation. We will analyze the mammary glands of Cav-1/StatSa double- knockout mice and study the ex vivo behavior of primary cultures of mammary epithelia from these mice. 2) Determine the role of StatSa activation and ER-alpha in Cav-1-related mammary tumorigenesis and metastasis. For this purpose, we will perform orthotopic transplantation of Met-1 cells expressing Cav-1 dominant-negative (DN) mutants (such as P132L) that are found in human breast cancer. The role of StatSa signaling will be assessed using DN mutants of StatSa and Jak-2. The role of estrogen will be assessed by ovariectomy and supplementation with estrogen pellets. Tamoxifen-resistance will also be investigated. 3) Determine if Cav-1 mutations co-segregate with StatSa activation in ER(+) human breast cancer samples. Here, we propose to examine the relevance of this newly defined signaling pathway in human breast cancer pathogenesis, using Cav-1 mutations, ER-alpha expression levels, and StatSa activation as novel prognostic markers. Since greater than 40% of ER-apha positive patients show tamoxifen-resistance, we will also examine if Cav-1 mutations and StatSa activation are critical predictors of tamoxifen-resistance.